---
id: metaflow
version: "2.19.37"
license: Apache Software License
license_treatment: permissive
maintenance: active
---

# metaflow — Metaflow: More AI and ML, Less Engineering

License: permissive · Maintenance: active · Downloads: 930.4K/mo

## What it is and what it does

Metaflow is a framework for managing the full lifecycle of AI and ML projects, from rapid local prototyping to production deployment. It provides a Pythonic API that unifies code, data, and compute management, allowing teams to write workflows once and scale them across local machines, cloud infrastructure, or production orchestrators without rewriting. The framework handles experiment tracking, versioning, dependency management, and both horizontal and vertical scaling with support for CPUs and GPUs.

Originally developed at Netflix and now supported by Outerbounds, Metaflow is designed for teams of any size working on classical statistics, deep learning, or foundation models. It reduces the engineering burden of moving from notebook prototypes to reliable production systems by providing built-in support for distributed computing, failure recovery, checkpointing, and reactive orchestration. The package depends on requests and boto3, making it suitable for cloud-native workflows, particularly on AWS.

Use it for:

- Prototype ML models locally in notebooks, then scale the same code to run on distributed cloud compute without modification
- Track experiments, versions, and artifacts across team members with built-in versioning and visualization
- Deploy data pipelines and ML workflows to production orchestrators with one-click deployment and event-driven triggering
- Run embarrassingly parallel or gang-scheduled compute jobs reliably across CPUs and GPUs with automatic failure recovery
- Manage dependencies and data access for large-scale data processing jobs handling petabytes of data

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Metaflow is a framework for building, prototyping, and deploying AI and ML systems, handling the full lifecycle from local development through production orchestration with support for scaling across cloud compute resources.

Yes. Metaflow is actively maintained, has low install friction, carries a permissive license, and is backed by proven production use at scale (Netflix runs 3000+ projects on it). It solves a real problem—bridging the gap between notebook prototyping and production ML systems—with a mature, well-documented API. Install if you're building ML workflows that need to scale beyond a single machine or move from experimentation to production.

## Install

pip install metaflow
uv add metaflow
poetry add metaflow

## Installing metaflow

Before you install: Low install friction with a pure-Python wheel distribution. Actively maintained with a recent release (3 days old) and strong community backing; the repository shows 10213 stars and continuous development.

License in practice: Licensed under Apache Software License (permissive), which allows commercial use, modification, and distribution with minimal restrictions—suitable for both proprietary and open-source projects.

Quickstart:

pip install metaflow

from metaflow import FlowSpec, step

class MyFlow(FlowSpec):
    @step
    def start(self):
        self.next(self.end)
    
    @step
    def end(self):
        pass

if __name__ == '__main__':
    MyFlow()

Verify before relying:

- Whether the package requires AWS/cloud credentials or infrastructure setup to use core features beyond local prototyping
- Performance characteristics and overhead when running on modest hardware or in resource-constrained environments
- Compatibility details with specific notebook environments (Jupyter, JupyterLab, etc.) beyond the stated notebook support

## Package facts

- License: Apache Software License (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 930.4K/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ml workflow orchestration, ai pipeline framework, machine learning deployment, data science workflow management, distributed computing for ml, experiment tracking and versioning, production ml orchestration, ml-orchestration, workflow-management, cloud-native

[View on SkillFed](https://skillfed.io/packages/metaflow) · [View on PyPI](https://pypi.org/project/metaflow/)